Wafer inspection systems help a semiconductor manufacturer increase and maintain integrated circuit (IC) chip yields by detecting defects that occur during the manufacturing process. One purpose of inspection systems is to monitor whether a manufacturing process meets specifications. The inspection system indicates the problem and/or the source of the problem if the manufacturing process is outside the scope of established norms, which the semiconductor manufacturer can then address.
Evolution of the semiconductor manufacturing industry is placing ever greater demands on yield management and, in particular, on metrology and inspection systems. Critical dimensions are shrinking while wafer size is increasing. Economics is driving the industry to decrease the time for achieving high-yield, high-value production. Thus, minimizing the total time from detecting a yield problem to fixing it determines the return-on-investment for the semiconductor manufacturer.
While greater emphasis is being placed on yield management, defect detection on semiconductor wafers can be complicated and time-consuming. Semiconductor manufacturers need improved techniques to detect defects in a faster and more reliable manner.
Review sampling can involve sampling defects from a wafer and sending them to a review system. Once the sampled defects are classified, normalization can be used to estimate the distribution of defect types for all defects. Normalization is a technique of judging defect type representation in all inspected data within a given data set when only a subset of this data is assigned class codes. Normalization can aid in understanding the impact of different defect types on all of the inspected data, especially when defect counts are reasonably large. This can cause the defect classification to be restricted to a sample set. Normalization is used in defect analyses such as defect type Pareto, defect source analysis, or statistical process control (SPC) monitoring.
Traditional review sampling samples defects based on random selection. However, certain high sensitivity inspections inherit the noise and, consequently, many inspected defects are considered to be a nuisance. Therefore, random sampling on noisy defect distribution will create high SNV (SEM non-visual) or no defect found results during review.
Traditional techniques allows normalization based on proportional or non-proportional sample size with respect to total defect count. These techniques use an arithmetic formula to estimate the distribution of unclassified defects to defect class by using the ratio of the number of defects in each class to the total number of classified defects. The results show numerical values instead of assigning individual unclassified defects with a class code. Results of these previous techniques are often inaccurate and do not represent the actual distribution of the defect classes. In the example of a normalized defect type Pareto, inaccurate ranking of a defect class may lead to wrong actions. Also, the normalization results cannot be used in defect-level yield prediction because individual defects cannot be normalized. Individual defects cannot be normalized because normalization involves an arithmetic calculation that uses the proportional percentage of the reviewed and classified defects to estimate the real distribution of the total population.
Therefore, improved defect review sampling and normalization is needed.